All the networks including the base networks are optimised using RMSPROP optimiser with \(3\times 10^{-4}\)  learning rate, \(0.99\)  squared gradient decay factor, and a batch size of 64 images per training iteration for the base networks and 512 images for the randECOC experiments. The implementation is performed using the Deep Learning Toolbox and MatConvNet [1]} within MATLAB, with a single NVIDIA GeForce GTX 1080 Ti 11GB graphics processing unit (GPU).
